AI's Four-Phase Barrier Establishment Strategy
In the realm of artificial intelligence (AI), agentic AI is setting a new standard for competitive moats. Unlike traditional software businesses where moats often emerge organically, agentic AI companies must deliberately architect their competitive position from the outset.
The process of building competitive moats in agentic AI is a long-term endeavour, spanning years, and follows a predictable pattern. This framework offers a roadmap for building sustainable advantages, which compound over time. The process is divided into three parts, each with its own strategic considerations.
The competitive moating process for agentic AI is structured into three phases, each with its own focus. Agentic AI platforms are built on a three-layer stack, moving across these layers.
The first layer, the systems of record, serves as the trusted core business data and enforcement. This layer provides a moat by owning rich, regulated, and longitudinal data that is costly and complex for others to replicate.
The second layer, the agent operating systems, orchestrates tasks and invokes AI tools. This layer leverages and protects proprietary data and custom AI agent orchestration, making it harder for competitors to duplicate.
The third and final layer, the outcome interfaces, consists of natural language and workflows embedded in daily user tools. This layer turns complexity into a competitive edge by seamlessly connecting siloed tools and automating multi-step processes.
Early adopters in agentic AI build moats through exclusive AI model training and optimizing scarce GPU compute resources, accelerating agent deployment advantages. Additionally, agentic AI agents do not just respond to requests but autonomously explore data, generate hypotheses, and learn continually, enabling insights and automation beyond static rule-based or manual workflows common in traditional software.
In contrast, traditional software businesses have moats built on feature differentiation within often siloed applications or suites, licensing models and locked-in legacy enterprise systems, network effects or platform lock-in without autonomous AI agents, and manual updates and human-driven workflows without continuous autonomous learning.
Shortcuts that undermine long-term strength should be resisted during the process of building moats in agentic AI. Success in each phase requires patience, strategic focus, and a long-term perspective. By adhering to this approach, agentic AI companies can establish and maintain a competitive edge in an ever-evolving technological landscape.
In the competitive moating process for agentic AI, the first layer, the systems of record, is designed to own rich, regulated, and longitudinal data, serving as a moat by making it costly and complex for others to replicate. Leveraging technology, specifically agent operating systems in the second layer, offers a strategic advantage through proprietary data and custom AI agent orchestration, making it harder for competitors to duplicate.
